Explore the vast range of titles available.

Drought, characterized by below-average water supply, profoundly affects regional water resources and various ecosystem services. The Palmer Drought Severity Index (PDSI) is a widely used metric for drought monitoring and climate change assessments but suffers from inherent climatic inconsistencies and lacks comprehensive and reliable estimates under changing climate conditions. Here we develop a monthly multi-model and multi-scenario dataset of self-calibrated PDSI for the period 1850–2094, derived from 11 climate model outputs within the Coupled Model Intercomparison Project 6 (PDSI_CMIP6). The traditional two-layer bucket model in PDSI is replaced with direct hydrological outputs from CMIP6 models, ensuring alignment with CMIP6 projections. The PDSI estimates are validated against soil moisture simulations through correlation and regression analysis. Application of the dataset reveals pronounced spatial heterogeneity in long-term drought trends across continents, with limited global-mean change but notable regional intensification under climate change. This dataset provides uncertainty-constrained quantifications of terrestrial moisture conditions in a changing climate, faithfully reflecting CMIP6-projected hydrological changes.

As fundamental data, gross domestic product (GDP) and electricity consumption can be used to effectively evaluate economic status and living standards of residents. Some scholars have estimated gridded GDP and electricity consumption. However, such gridded data have shortcomings, including overestimating real GDP growth, ignoring the heterogeneity of the spatiotemporal dynamics of the grid, and limited time-span. Simultaneously, the Defense Meteorological Satellite Program’s Operational Linescan System (DMSP/OLS) and National Polar-orbiting Partnership’s Visible Infrared Imaging Radiometer (NPP/VIIRS) nighttime light data, adopted in these studies as a proxy tool, still facing shortcomings, such as imperfect matching results, discontinuity in temporal and spatial changes. In this study, we employed a series of methods, such as a particle swarm optimization-back propagation (PSO-BP) algorithm, to unify the scales of DMSP/OLS and NPP/VIIRS images and obtain continuous 1 km × 1 km gridded nighttime light data during 1992–2019. Subsequently, from a revised real growth perspective, we employed a top-down method to calculate global 1 km × 1 km gridded revised real GDP and electricity consumption during 1992–2019 based on our calibrated nighttime light data.Measurement(s)GDP • electricty consumptionTechnology Type(s)machine learning

The wild apple, Malus fusca accession MAL0045, is highly resistant to fire blight disease, caused by the bacterial pathogen, Erwinia amylovora. A major resistance locus, FB_Mfu10 was identified on chromosome 10 of MAL0045 including other contributory loci on chromosomes 16, 4, and 15. Here, we report a chromosome-scale genome assembly of MAL0045 to facilitate the studies of its fire blight resistance. PacBio sequencing and Illumina sequencing for Hi-C contig anchorage were employed to obtain the genome. A total of 669.46 Mb sequences were anchored onto 17 chromosomes, taking up 99.75% of total contig length. Contigs anchored onto chromosomes were further ordered and orientated, where a total of 637.67 Mb sequences were anchored onto chromosomes in proper order and orientation, resulting in a final anchoring ratio of 95.25%. The BUSCO score of this assembly is 97.46%. Further, a total of 47,388 genes were predicted via ab initio, homology-based, and RNAseq methodologies. The availability of this genome will facilitate functional and comparative genomics studies, especially about the donors of fire blight resistance in Malus.

Seasonal agricultural drought monitoring systems, which rely on satellite remote sensing and land surface models (LSMs), are important for disaster risk reduction and famine early warning. These systems require the best available weather inputs, as well as a long-term historical record to contextualize current observations. This article introduces the Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System (FLDAS), a custom instance of the NASA Land Information System (LIS) framework. The FLDAS is routinely used to produce multi-model and multi-forcing estimates of hydro-climate states and fluxes over semi-arid, food insecure regions of Africa. These modeled data and derived products, like soil moisture percentiles and water availability, were designed and are currently used to complement FEWS NET’s operational remotely sensed rainfall, evapotranspiration, and vegetation observations. The 30+ years of monthly outputs from the FLDAS simulations are publicly available from the NASA Goddard Earth Science Data and Information Services Center (GES DISC) and recommended for use in hydroclimate studies, early warning applications, and by agro-meteorological scientists in Eastern, Southern, and Western Africa. Design Type(s) data integration objective • longitudinal data analysis • observation design Measurement Type(s) ecological observations Technology Type(s) digital curation Factor Type(s) Sample Characteristic(s) Earth • Sub-Saharan Africa • vegetation layer • albedo • elevation • soil • hydrological process • land • atmosphere • hydrological precipitation process Machine-accessible metadata file describing the reported data (ISA-Tab format)

The coastline reflects coastal environmental processes and dynamic changes, serving as a fundamental parameter for coast. Although several global coastline datasets have been developed, they mainly focus on coastal morphology, the typology of coastlines are still lacking. We produced a Global CoastLine Dataset (GCL_FCS30) with a detailed classification system. The coastline extraction employed a combined algorithm incorporating the Modified Normalized Difference Water Index and an adaptive threshold segmentation method. The coastline classification was performed a hybrid transect classifier that integrates a random forest algorithm with stable training samples derived from multi-source geophysical data. The GCL_FCS30 offers significant advantages in capturing artificial coastlines, reflecting strong alignment with location validation data. The GCL_FCS30 classification was found to achieve an overall accuracy and Kappa coefficient over 85% and 0.75. Each coastline category accurately covered the majority of the area represented in third-party data and exhibited a high degree of spatial relevance. Therefore, the GCL_FCS30 is the first global coastline category dataset covering the high latitudes in a continuous and smooth line vector format.

The global marine gravity anomalies are primarily recovered from geoid gradients in the along-track directions obtained through satellite altimetry. However, the accuracy of the gravity model is significantly constrained by the sparse geoid gradients in the cross-track directions. To overcome the scarcity of cross-track geoid gradients, we employ a mean sea surface model to calculate geoid gradients in multiple directions, thereby recovering marine gravity anomalies. A global marine gravity anomaly model with 1-arcmin grid was determined from an optimized mean sea surface model. The accuracy of this gravity anomaly model was assessed by shipborne gravity and released marine gravity anomaly models. By the combination of geoid gradients in multiple directions, the meridian components and the prime vertical components of deflections of the vertical achieved consistent accuracy. The accuracy of the gravity anomaly model is 4.31 mGal, derived from shipborne gravity anomalies. Furthermore, the accuracy of this model has been validated across various regions, encompassing different latitudes, bathymetry, and distances from the coastline. The new gravity anomaly model slightly outperforms the released model.

The Qilian Mountain Area (QMA) serves as a crucial ecological barrier and strategic water conservation zone in China. Recent years have seen heightened social attention to environmental issues within the QMA, underscoring the need for accurate and continuous land cover maps to support ecological monitoring, analysis, and forecasting. This paper presents the QMA_LC30 dataset, which includes 9 land cover categories and spans the period from 1990 to 2020, with updates every 5 years. The dataset primarily utilizes 30 m Landsat series data and features: 1) High precision, achieved through a geographical division and hierarchical classification decision tree approach, complemented by visual interpretation. 2) Robust consistency, ensured by a change detection method based on a benchmark map. The QMA_LC30 dataset undergoes rigorous accuracy validation, achieving an overall accuracy of over 0.92 for all 7 periods of land cover maps. Compared to GlobeLand30, ESA WorldCover, ESRI 2020 Land Cover, FROM_GLC30, and GLC_FCS30, QMA_LC30 demonstrates the highest consistency with remote sensing images.

Unlike satellite images, which are typically acquired and processed in near-real-time, global land cover products have historically been produced on an annual basis, often with substantial lag times between image processing and dataset release. We developed a new automated approach for globally consistent, high resolution, near real-time (NRT) land use land cover (LULC) classification leveraging deep learning on 10 m Sentinel-2 imagery. We utilize a highly scalable cloud-based system to apply this approach and provide an open, continuous feed of LULC predictions in parallel with Sentinel-2 acquisitions. This first-of-its-kind NRT product, which we collectively refer to as Dynamic World, accommodates a variety of user needs ranging from extremely up-to-date LULC data to custom global composites representing user-specified date ranges. Furthermore, the continuous nature of the product’s outputs enables refinement, extension, and even redefinition of the LULC classification. In combination, these unique attributes enable unprecedented flexibility for a diverse community of users across a variety of disciplines.Measurement(s)land use • land coverTechnology Type(s)deep learning

We describe the latest version of the NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP-CMIP6). The archive contains downscaled historical and future projections for 1950–2100 based on output from Phase 6 of the Climate Model Intercomparison Project (CMIP6). The downscaled products were produced using a daily variant of the monthly bias correction/spatial disaggregation (BCSD) method and are at 1/4-degree horizontal resolution. Currently, eight variables from five CMIP6 experiments (historical, SSP126, SSP245, SSP370, and SSP585) are provided as procurable from thirty-five global climate models.Measurement(s)temperature of air • volume of hydrological precipitation • humidity • stellar radiation • atmospheric wind speedTechnology Type(s)statistical downscalingSample Characteristic - Environmentatmosphere

We present new global maps of the Köppen-Geiger climate classification at an unprecedented 1-km resolution for the present-day (1980-2016) and for projected future conditions (2071-2100) under climate change. The present-day map is derived from an ensemble of four high-resolution, topographically-corrected climatic maps. The future map is derived from an ensemble of 32 climate model projections (scenario RCP8.5), by superimposing the projected climate change anomaly on the baseline high-resolution climatic maps. For both time periods we calculate confidence levels from the ensemble spread, providing valuable indications of the reliability of the classifications. The new maps exhibit a higher classification accuracy and substantially more detail than previous maps, particularly in regions with sharp spatial or elevation gradients. We anticipate the new maps will be useful for numerous applications, including species and vegetation distribution modeling. The new maps including the associated confidence maps are freely available via www.gloh2o.org/koppen.